Quantitative Validation of Rating Models for Low Default Portfolios through Benchmarking: Basel II

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  • Topic: Credit default swap, Credit rating, Basel II
  • Pages : 17 (4604 words )
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  • Published : April 9, 2012
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Quantitative Validation of Rating Models for Low Default Portfolios through Benchmarking The new capital adequacy framework (Basel II) is one of the most fiercely debated topics the financial sector has seen in the recent past. Following a consultation process that lasted several years, the regulations formally took effect on January 1, 2007. The advanced approaches (the advanced internal ratings-based, or A-IRB, approach and the advanced measurement approach, or AMA) are scheduled to become operational on January 1, 2008. The new framework allows banks to use the IRB approach for the calculation of the assessment base for credit risk. Use of the IRB approach is subject to regulatory approval, which can only be obtained if the internal rating systems meet certain requirements. One of these requirements is that the models employed must have good predictive power. Banks must review this predictive power once a year by performing a qualitative and quantitative validation of the models. The statistical methods used to perform quantitative validation require a significant amount of default data to derive valid statements about the model, but such data are typically scarce in the case of rating models for so-called low default portfolios (LDPs), i.e. portfolios for which banks have little default history. In this paper, we first deal with the general problems of LDPs under the IRB approach and cover the problems of validating rating models for LDPs. We then present an alternative method for the quantitative validation of such models, based on the idea of benchmarking. Finally, we provide an example of the application of the proposed validation method. JEL classification: G20, C19 Keywords: rating models, validation, benchmarking, low default portfolios Markus Ricke, Georg von Pföstl

1 Introduction Following a consultation process that lasted several years, the Basel Committee on Banking Supervision (BCBS) published the revised framework “International Convergence of Capital Measurement and Capital Standards” (Basel II) in June 2004. The Capital Requirements Directive, comprising the recast EU directives 2006/48/EC and 2006/49/EC, transposed the Basel II provisions into EU law. These directives, in turn, were transposed into Austrian law by amending the Austrian Banking Act (Bankwesengesetz – BWG) in August 2006 and by publishing the new Solvency Regulation (Solvabilitätsverordnung – SolvaV) and Disclosure Regulation (Offenlegungsverordnung – 1

OffV) in October 2006. The Basel II revised international capital framework finally entered into force in Austria on January 1, 2007.1 The new framework allows banks to use the IRB approach for the calculation of the assessment base for credit risk (IRB approach under Article 22b Austrian Banking Act), subject to regulatory approval, which can only be obtained if the internal rating systems meet a number of requirements that are defined under Article 37 ff. of the Solvency Regulation. One of these requirements stipulates that banks must demonstrate that their rating models have good predictive power, and that the model must be quantitatively and qualitatively validated on an annual basis

By exercising areas of national discretion, Austrian banks can postpone the application of the new regulations to January 1, 2008.

Financial Stabilit y Report 14



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Quantitative Validation of Rating Models for Low Default Portfolios through Benchmarking

(Articles 41 and 59 of the Solvency Regulation). The statistical methods typically used to perform quantitative validation require a significant amount of default data to derive valid statements about the model, which may be problematic in the case of rating models for low default portfolios (LDPs), i.e. portfolios for which banks have little default history, e.g. sovereigns. Therefore, this paper presents an alternative method for the quantitative validation of rating models that can be used to assess the predictive power of...
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